Soil moisture forecast for smart irrigation: The primetime for machine learning
作者：
Highlights：
• IoT and machine learning promote soil moisture forecast for smart irrigation.
• Our approach is fully data-driven and independent of domain-knowledge features.
• Results come from twelve fields on four farms, from eight crop types in four years.
• Boosting outperforms neural networks, and blended models improve performance.
• Poor data quality is a challenge for machine learning soil moisture forecast.
摘要
•IoT and machine learning promote soil moisture forecast for smart irrigation.•Our approach is fully data-driven and independent of domain-knowledge features.•Results come from twelve fields on four farms, from eight crop types in four years.•Boosting outperforms neural networks, and blended models improve performance.•Poor data quality is a challenge for machine learning soil moisture forecast.
论文关键词：Smart irrigation,Machine learning,Soil moisture forecast,Water need estimation,Internet-of-Things
论文评审过程：Received 9 December 2021, Revised 14 April 2022, Accepted 27 May 2022, Available online 31 May 2022, Version of Record 1 July 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.117653
